Blood-based proteomic profiling identifies OSMR as a novel biomarker of AML outcomes

Inflammation is increasingly recognized as a critical factor in acute myeloid leukemia (AML) pathogenesis. We performed blood-based proteomic profiling of 251 inflammatory proteins in 543 patients with newly diagnosed AML. Using a machine learning model, we derived an 8-protein prognostic score termed the leukemia inflammatory risk score (LIRS). Individual proteins were evaluated in multivariable Cox models, and model performance was assessed by cumulative concordance index. Findings were validated in internal and external cohorts across 2 institutions. Blood-based LIRS significantly outperformed the European LeukemiaNet 2022 risk model and was independently prognostic of overall survival after accounting for known clinical and molecular prognostic factors. Oncostatin M receptor was uniquely identified as the strongest independent predictor of survival, early mortality, and induction chemotherapy response, and further validated in an independent assay. These blood-based biomarkers could have significant clinical implications for risk stratification and prognostication in patients with newly diagnosed AML.

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Publication Details

Journal
Blood
Published
2025-04-05
DOI
https://doi.org/10.1182/blood.2024027244
Citations
9
Primary Topic
Acute Myeloid Leukemia Research
Type
article
Field-Weighted Citation Impact
15.93

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article

Blood-based proteomic profiling identifies OSMR as a novel biomarker of AML outcomes

Jennifer Marvin‐Peek, Courtney Denton Dinardo, Ivo Veletić, Steven Mitchell Kornblau et al.
9 citations
Blood
Acute Myeloid Leukemia Research
15.93
article

Blood-based proteomic profiling identifies OSMR as a novel biomarker of AML outcomes

Jennifer Marvin‐Peek, Courtney Denton Dinardo, Ivo Veletić, Steven Mitchell Kornblau, Tapan Mahendra Kadia, Mark D. Minden, Koji Sasaki, Patrick K. Reville, Naval Guastad Daver, Bin Yuan, Yiwei Liu, Jessica L. Root, Bofei Wang, Philip L. Lorenzi, Farhad Ravandi, Hussein A. Abbas, Yu-Jen Kuo, Andrea Arruda, Wei Qiao, Nicholas James Short, Araceli Isabella Garza, H. Kantarjian
article en
9 citations

Abstract

Inflammation is increasingly recognized as a critical factor in acute myeloid leukemia (AML) pathogenesis. We performed blood-based proteomic profiling of 251 inflammatory proteins in 543 patients with newly diagnosed AML. Using a machine learning model, we derived an 8-protein prognostic score termed the leukemia inflammatory risk score (LIRS). Individual proteins were evaluated in multivariable Cox models, and model performance was assessed by cumulative concordance index. Findings were validated in internal and external cohorts across 2 institutions. Blood-based LIRS significantly outperformed the European LeukemiaNet 2022 risk model and was independently prognostic of overall survival after accounting for known clinical and molecular prognostic factors. Oncostatin M receptor was uniquely identified as the strongest independent predictor of survival, early mortality, and induction chemotherapy response, and further validated in an independent assay. These blood-based biomarkers could have significant clinical implications for risk stratification and prognostication in patients with newly diagnosed AML.

BloodVol. 145(25)
The University of Texas MD Anderson Cancer Center (US), Princess Margaret Cancer Centre (CA)
GlaxoSmithKline, Cancer Prevention and Research Institute of Texas, National Institutes of Health, University of Texas MD Anderson Cancer Center, National Cancer Institute
Good health and well-being
Openalex Percentile: Top 1%
Acute Myeloid Leukemia Research
15.93
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